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International Conference on Advances in Information technology (ICAIT)

作者： G S Pradeep Ghantasala Bhuvan Unhelkar Prasun Chakrabarti Pellakuri Vidyullatha Meghana Pyla Department Of Information Systems and Decision Sciences Muma College of Business University of South Florida South Florida USA Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bengaluru India Department of Computer Science and Engineering Sir Padampat Singhania University Udaipur Rajasthan India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur Andhra Pradesh India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

One of the prominent causes of death for women universally is breast cancer. The number of early deaths is reduced with early detection. Using an ultrasound scan, the data examine the ultrasound images of breast cancer. Three categories, including normal, benign, and malignant images, comprise the Breast Ultrasound Dataset. Incorporating machine learning with breast ultrasound images might yield significant outcomes for the identification, classification, and segmentation of breast cancer. Through a mediocre image size of 500 500 pixels, the collection comprises 750 images. Attention U-Net/U- Net is the best-known model for Multi-Class Segmentation to create an attention U-Net Model. The Model will consist of a Decoder Block Encoder Block and an Attention Gate. The attention gate layers have neurons with sizes of 256, 128, and 64, which typically correspond to the number of channels in the feature maps. The output layer in the U-Net model characteristically consists of a single convolutional layer through a kernel size of 1 1. The activation function used in the output layer was sigmoid; whichever is collective for binary classification tasks like image segmentation. The sigmoid activation function outputs values between 0 and 1, representing the possibility of each pixel fitting to the target class. The loss function stated is “binary_crossentropy,” which is appropriate in binary classification tasks. The optimizer specified has Adam, which is well-liked for neural network training because of its adaptable learning rate characteristics. In contrast to conventional gradient descent techniques, the Adam optimizer adjusts the learning rate during training, which might result in faster convergence and improved results with 98% accuracy at epoch 17 with a validation loss of 0.05.

关键词： Training Image segmentation Ultrasonic imaging Protocols Neurons Imaging Standardization Logic gates Breast cancer Planning

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Machine Learning Based Ensemble Classifier using Wisconsin Dataset For Breast Cancer Prediction

Machine Learning Based Ensemble Classifier using Wisconsin D...

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： G S Pradeep Ghantasala Anjaneyulu Kunchala Sathiyaraj R Venkateswarulu Naik. B Yaswanth Raparthi P Vidyullatha Faculty of Science & Technology Alliance College of Engineering and Design Alliance University Bengaluru India Department of Computer Science and Engineering GITAM School of Technology GITAM University Bangalore Karnataka India Computer Science Engineering Narasimha Reddy Engineering College Hyderabad India Computer Science Engineering Vellore Institute of Technology Vellore TN India Computer Science Engineering Koneru Lakshmaiah Education foundation Guntur A.P India

Subclass of Artificial Intelligence, machine learning integrates several optimizations, probabilistic, and statistical methodologies to help computers learn from prior occurrences and find it challenging to spot patterns in intricate datasets, noised and large datasets. As a result, machine learning in cancer diagnosis and detection has increased. In women, breast cancer is the utmost common malignancy. The study aims to predict breast cancer by comparing widely used machine learning algorithms and methodologies, neural networks, Random Forest, and Boosting, upon Wisconsin Diagnosis Breast Cancer data set to inspect the performance of critical characteristics such as precision and accuracy. This study’s accurate, competitive results can be used for detection and treatment.

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Machine Learning Algorithms for the Detection of Threats in IoT Healthcare

Machine Learning Algorithms for the Detection of Threats in ...

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Emerging Research in Computational Science (ICERCS), International Conference on

作者： Arun V P. Shenbagavalli Sridhar T Manivannan B Mahesh T R K Anitha Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Division of Digital Sciences Karunya Institute of Technology and Sciences India Alliance College of Engineering and Design Alliance University Bangalore India Computer Science and Engineering Vivekanandha College of Engineering For Women(Anna University) Namakkal India Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore India Department of Information Technology CSI College of Engineering Ooty Tamilnadu

IoT (Internet of Things) technology enables the connectivity and communication of healthcare-related equipment, medical devices, and sensors. This connectivity enables better monitoring, data collecting, and analysis, which leads to better patient outcomes and more effective healthcare delivery. However, with the rising use of linked devices, it is critical to detect and prevent Cyberattacks in order to secure sensitive patient information. In order to tackle this problem, the article delves into the application of machine learning methods like Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Logistic Regression (LR), Random Forest (RF), and Naive Bayes (NB), These algorithms were evaluated using the WUSTL EHMS2020 dataset and demonstrated the highest level of accuracy.

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IoT based Automated Rehabilitation using 3D Printed Hand and Health Monitoring for Paralyzed Patients

IoT based Automated Rehabilitation using 3D Printed Hand and...

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Data Intelligence and Cognitive Informatics (ICDICI), International Conference on

作者： S. Govindaraj S. Maheswari P. Loganathan J. S. Sharanya Nivasini M. Ponkarthika Department of Biomedical Engineering Velalar College of Engineering and Technology Erode Department of Electronics and Communication Engineering Excel Engineering College Namakkal Department of Computer Science and Design Erode Sengunthar Engineering College Erode Department of Electronics and Communication Engineering Kongu Engineering College Erode

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

Paralysis is a condition characterized by the loss of muscle function in part of the body, leading to impaired movement and control. It can significantly impact an individual's daily life, mobility, and overall well-being. Paralysis can result from various factors, including spinal cord injuries, strokes, traumatic injuries, neurological disorders, and certain medical conditions. According to the World Health Organization (WHO), millions of people worldwide live with some form of paralysis, with varying degrees of severity. Tracking the improvement of patients using these techniques might be difficult. Rehabilitation results inadequate monitoring of critical health factors including temperature and blood oxygen levels. The suggested approach is creating a 3D - printed hand incorporating Internet of Things sensors. Paralysis patients can benefit from this hand real-time sensory feedback and customized therapy routines. IoT sensors are incorporated in the hand to track the patient's hand movements, blood oxygen saturation (SpO2), heart rate, and muscle activity (EMG). Arduino UNO serves as the central controller that facilitates automation, while the Wi-Fi module and voice control enhance user interaction. Additionally, Peltier modules keep the affected extremities at their ideal temperature, improving patient comfort and promoting blood flow. Suggested approach provides a multi-dimensional approach to enhance their entire quality of life and health condition for people with this difficult medical condition by combining all those features.

关键词： Hands Tracking Muscles Three-dimensional printing Real-time systems Paralysis Sensors Monitoring Blood Wireless fidelity

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Intrusion detection using optimized ensemble classification in fog computing paradigm

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Knowledge-Based Systems 2022年 252卷

作者： Ramkumar, M.P. Daniya, T. Mano Paul, P. Rajakumar, S. Department of Computer Science and Engineering Thiagarajar College of Engineering Madurai625015 India Department of Information Technology GMR Institute of Technology Andhra Pradesh Rajam India Alliance College of Engineering and Design Alliance University Karnataka - 562106 Bangalore India Department of ECE Panimalar Engineering College Chennai India

Fog computing is a framework, which expands services of the cloud to network for addressing inherent issues of the cloud. Intrusion Detection Systems (IDSs) represent a major unit of the security model for fog networks for facilitating service quality. This paper devises a hybrid optimization-driven ensemble classifier in a fog computing platform. In fog computing, the complete processing is done utilizing trio layers that include the cloud layer, end point layer, and fog layer. In the cloud layer, three procedures, like data transformation, selection of features, and classification processes were carried out. A data transformation is conducted with log transformation. A feature is chosen using Kolmogorov–Smirnov correlation-based filter. Then, the classification is done utilizing ensemble classifiers, named RideNN, Deep Neuro-Fuzzy Network (DNFN), and Shepard convolutional neural network (ShCNN). The tuning of the ensemble classifier is conducted utilizing the developed Rider Sea Lion Optimization (RSLO) algorithm. RSLO algorithm is conceived by amalgamating the Rider optimization algorithm (ROA) and Sea Lion Optimization Algorithm (SLnOA). At an end point layer, a physical process is done. In the fog layer, intrusion detection is done based on a trained ensemble classifier. The proposed RSLO-based ensemble approach provides enhanced performance with higher precision of 88%, recall of 88.4%, F-measure of 88.2%, and accuracy of 97.2%. © 2022 Elsevier B.V.

关键词： Intrusion detection

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Abnormality Detection of Retinal Images Using Dual Autoencoder 21

Abnormality Detection of Retinal Images Using Dual Autoencod...

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21st IEEE India Council International Conference, INDICON 2024

作者： Karthikayan Nagarajan, P. Bairwa, Sachin Kumar Lakshmi Priya, A. Jayaraman, Umarani Indian Institute of Information Technology Design and Manufacturing Department of Computer Science and Engineering Kancheepuram Chennai India Psg College of Technology Department of Biomedical Engineering Coimbatore India

ISBN: (纸本)9798350391282

This paper presents a dual autoencoder model for abnormality detection from retinal fundus images. In this paper, the dual autoencoder along with (Kernal Density Estimation) KDE is used to improve the result as compared to a single autoencoder with MSE for disease classification. The proposed model makes use of an autoencoder because it can be trained on unlabeled data. It is a good fit for abnormality detection scenarios in which access to labeled data may be limited or costly. It is useful to have a model that does not require labeled examples of abnormalities because abnormalities are usually rare and diverse. Dual autoencoders combined with Kernel Density Estimation (KDE) offer a powerful framework for abnormality detection by leveraging the complementary strengths of both techniques. The dataset used in this research is the Ocular Disease Intelligent Recognition (ODIR) which is a structured ophthalmic database with seven classes of abnormal covering most of the common retinal eye diseases. The evaluation criteria used in this work include MSE for reconstruction error and accuracy for abnormality detection. © 2024 IEEE.

关键词： Abnormality Detection Disease Classification Dual Autoencoder Eye Disease ODIR Dataset Retinal Images

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A Novel Approach to Identify Pituitary Tumor Using an Optimal CNN Model with Deep Learning

A Novel Approach to Identify Pituitary Tumor Using an Optima...

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Computational Intelligence, Networks and Security (ICCINS), International Conference on

作者： Ebin P M Roja Thomas Safad Ismail Kavitha Nair R Alliance School of Advance Computing Alliance University Banglore India Department of Computer Science and Engineering Mangalam College of Engineering Ettumanoor India Department of Computer Engineering College of Engineering Chengannur Chengannur India Department of AI &ML Acharya Institute of Technology Banglore India

The technical paper introduces a novel approach to identifying pituitary tumors using an Optimized Convolutional Neural Network (OCNN) model with deep learning. Pituitary tumors rank among the most prevalent variety of brain tumors, and accurate identification is critical for proper diagnosis and treatment. The suggested method makes use of a deep learning framework that combines an ideal feature extraction method with a pre-trained CNN model. The pulled out features are then used to train the CNN model to classify pituitary tumors accurately. The effectiveness of the suggested method is assessed using a publicly accessible dataset, revealing that it surpasses the performance of current leading techniques. The suggested method possesses the capacity to act as a valuable asset for making clinical decisions in the field of neuroimaging, particularly for the accurate identification of pituitary tumors. The results of the experiment indicate that our suggested model successfully detects and classifies tumors with high accuracy, sensitivity, and specificity. Our model specifically achieves a 99.55% overall accuracy, 99% sensitivity, 99% specificity and 99% F1 score. These outcomes show that our suggested CNN design for brain tumor identification and classification performs better than several state-of-the-art techniques.

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Automated CKD Detection via Image Processing Employing Delta-E Metric and CNN

Automated CKD Detection via Image Processing Employing Delta...

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IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)

作者： Espin Shalo S P Jeeshan R J S Gunasekar Pius Agbulu G Venkat Ramana N Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai Tamilnadu India Department of Computer Science and Design Rajalakshmi Engineering College Chennai Tamilnadu India Department of Mechatronics Engineering Chinhoyi University of Technology Zimbabwe Department of Electrical Engineering Indian Institute of Technology Chennai Tamilnadu India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510039

Chronic Kidney Disease (CKD) is a global health concern that often progresses silently until advanced stages, making early detection critical. This paper presents a point-of-care system that automates the color comparison between a test strip and a reference strip to detect biomarkers like albumin associated with kidney dysfunction. The system leverages mobile-captured images, applying image processing techniques such as segmentation and color extraction, and compares the test strip's top color with reference colors on the parent strip using the Delta-E metric in the LAB color space. The closest match quantifies the biomarker concentration, providing a reliable diagnostic measure. To enhance diagnostic precision, this research incorporates Convolutional Neural Networks (CNNs) to refine image preprocessing and anomaly detection. The CNN model complements existing methods by mitigating environmental influences like lighting variability and improving feature extraction accuracy. This hybrid approach ensures consistency while adapting to diverse datasets and image conditions. The result is a cost-effective, fast, and accessible diagnostic tool that reduces manual errors and supports early CKD detection in resource-limited settings. Future work will focus on expanding the application of this system to detect other diseases and further optimize the integration of CNNs for enhanced scalability and robustness.

关键词： Measurement Strips Accuracy Image color analysis Scalability Lighting Biomarkers Chronic kidney disease Robustness Convolutional neural networks

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Promoting Constructive Online Debates Through Toxic Comment Classifier

Promoting Constructive Online Debates Through Toxic Comment ...

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IT Innovation and Knowledge Discovery (ITIKD), International Conference on

作者： Jeevanandam Jotheeswaran V Geetha M. Iyyappan K.G. Srinivasa Department of Computer Science and Engineering Alliance University Bengaluru India Department of Computer Science Alliance University Bengaluru India College of Information Technology Ahlia University Kingdom of Bahrain Department of Data Science and Artificial Intelligence IIIT Naya Raipur Chhattisgarh India

ISBN: (数字)9798350355468

ISBN: (纸本)9798350355475

The widespread use of online platforms for communication has given rise to the issue of toxic comments, which can have detrimental effects on individuals and communities. In this research, we present a toxic comment classifier that aims to automatically identify and classify toxic comments with high accuracy. We leverage a dataset consisting of annotated toxic comments, employing a mix of machine learning methods and natural language processing techniques. Through comprehensive testing and evaluation, we show that our method is effective in correctly identifying harmful comments. Our classifier achieves F1-score exceeding 85% across different toxicity categories. The developed toxic comment classifier has the potential to contribute to the creation of safer and more inclusive online environments, enabling proactive moderation and targeted intervention. This study emphasises how crucial it is to use machine learning approaches to fight toxic behaviour and promote constructive online discussion.

关键词： Technological innovation Toxicology Automation Accuracy Machine learning Nearest neighbor methods Knowledge discovery Natural language processing Convolutional neural networks Testing

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Abnormality Detection of Retinal Images Using Dual Autoencoder

Abnormality Detection of Retinal Images Using Dual Autoencod...

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IEEE India Conference (INDICON)

作者： Karthikayan Nagarajan P Sachin Kumar Bairwa Lakshmi Priya A Umarani Jayaraman Department of Computer Science and Engineering Indian Institute of Information Technology Design and Manufacturing Kancheepuram Chennai India Department of Biomedical Engineering PSG College of Technology Coimbatore India

ISBN: (数字)9798350391282

ISBN: (纸本)9798350391299

关键词： Accuracy Autoencoders Estimation Retina Eye diseases Transformers Kernel Image reconstruction Unsupervised learning Biomedical imaging

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